Show HN: Ckg-agentforce – Salesforce AgentForce as a traversable MCP graph

A developer released ckg-agentforce, an open-source tool that represents Salesforce AgentForce as a traversable knowledge graph with 40 nodes and 52 edges, enabling deterministic, auditable answers about AgentForce dependencies, billing paths, and trust layer policy without model inference. The graph pins every edge to a SHA-256-hashed source document, allowing verification that source content has not changed since extraction, addressing the problem of costly failed autonomous resolutions that cost $2 per attempt.

ckg-agentforce — AgentForce as a traversable knowledge graph An auditable knowledge graph for Salesforce AgentForce — deterministic agent answers with full source traceability. AgentForce charges $2 per autonomous resolution . Every failed resolution is a retry, a CSAT hit, and $2 with no outcome. Wrong inference is expensive here. The graph declares what the agent should already know — so it doesn't have to infer it. Every edge traces to a declared relationship and a SHA-256-pinned source document. Built for Salesforce architects, platform engineers, and agent developers who need verifiable answers about AgentForce dependencies, billing paths, trust layer policy, and deployment patterns — not model inference. Not a general-purpose semantic search layer. If it's not a declared edge, the graph doesn't return it. pip install ckg-agentforce or: uvx ckg-agentforce PyPI https://pypi.org/project/ckg-agentforce/ · GitHub https://github.com/Yarmoluk/ckg-agentforce · Benchmark paper https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf · graphifymd.com https://graphifymd.com 40 nodes · 52 edges · the full AgentForce stack as a typed dependency graph. Pre-structured, traversable, deterministic. Served over MCP. No inference at query time. get prerequisites "Autonomous Resolution" → Autonomous Resolution ← $2/event billing trigger ├─ REQUIRES Resolution Criteria ├─ REQUIRES Audit Trail │ └─ REQUIRES Einstein Trust Layer │ ├─ REQUIRES AgentForce Platform │ └─ REQUIRES Reasoning Engine └─ REQUIRES Policy Enforcement └─ REQUIRES Einstein Trust Layer 269 tokens · declared edges only · no inference RAG equivalent: ~2,982 tokens · probabilistic query ckg "Einstein Trust Layer" → Dependents what it gates : ← REQUIRES Data Masking ← REQUIRES Audit Trail ← REQUIRES Zero Data Retention ← REQUIRES Grounding ← REQUIRES Einstein Agent ← REQUIRES Policy Enforcement Six capabilities gate on this one node. RAG returns six separate docs. The graph knows — it's a declared edge. Every node carries a source url and a source hash SHA-256 of the source document's bytes at extraction time . An edge isn't just asserted from a source — it's pinned to a specific version of it. Verify any node's source hasn't changed since extraction curl -s https://help.salesforce.com/s/articleView?id=sf.einstein ai trust layer.htm | sha256sum expected: cc11eedeee761e083a591cd20bbbdf46d2942906519dc5f8e51e617857118cda The full audit chain: edge answer → graph commit hash git log -- agentforce.csv → source content hash sha256 of page bytes at extraction time → knowledge source ref URL — fetch hint, not trust anchor A hash mismatch means either the source changed stale edge → re-extract or the graph was patched without re-fetching silent edit → investigate . No judgment required. Run scripts/refresh hashes.py to recompute. Via MCP — verify source "Einstein Trust Layer" : source url: https://help.salesforce.com/s/articleView?id=sf.einstein ai trust layer.htm source hash: sha256:cc11eedeee761e083a591cd20bbbdf46d2942906519dc5f8e51e617857118cda verify: curl -s '<url ' | sha256sum Reference implementation of knowledge source ref + source content hash from GuardrailDecisionV1 https://github.com/Yarmoluk/ckg-nvidia-ai/blob/main/docs/guardrail-decision-v1.md . Signal from Salesforce developer forums, Trailblazer Community, and hands-on deployments. 01 — The $2 retry problem. An agent misidentifies the resolution path. It attempts resolution, fails criteria, retries — $4 spent, zero outcome. The billing trigger lives four hops from Einstein Agent. Agents that don't traverse the full chain get it wrong. 02 — "Which policy tier is blocking my agent?" Einstein Trust Layer gates six downstream capabilities. Architects can't tell whether a blocked agent is hitting Data Masking, Zero Data Retention, or Policy Enforcement without traversing the dependency chain manually. The graph makes it a one-hop query. 03 — The Grounding Source Gap. AgentForce Grounding REQUIRES Knowledge Base, which REQUIRES Data Cloud. Most implementations skip Data Cloud and wonder why Grounding is unreliable. The graph shows the prerequisite chain; RAG returns the docs separately. 04 — NVIDIA NIM integration path. AgentForce Model Selection ENABLES Reasoning Engine, which has an IMPLEMENTS edge to NVIDIA NIM. Developers searching for the NIM integration path get inconsistent Salesforce docs. The declared edge makes it deterministic. Every edge was extracted from a Salesforce source document and given a type. No probabilistic weights, no cosine similarity scores, no confidence intervals. An edge either exists — declared, typed, sourced — or it doesn't. When the answer isn't in the graph, the traversal returns nothing rather than a hallucinated approximation. Edge types: | Type | Meaning | Example | |---|---|---| | REQUIRES | Hard prerequisite — A cannot function without B | Einstein Trust Layer REQUIRES AgentForce Platform | | ENABLES | Capability unlock — A makes B possible | Model Selection ENABLES Reasoning Engine | | IMPLEMENTS | Concrete instantiation of an abstract concept | NVIDIA NIM IMPLEMENTS Reasoning Engine | | RELATES TO | Conceptual proximity, no dependency direction | Data Masking RELATES TO Zero Data Retention | Why no confidence levels? The edge type is the confidence signal. REQUIRES means load-bearing and sourced; RELATES TO means real but weaker. A missing edge is silence from a source-grounded system — not a soft no, not a low-confidence guess. ✗ RAG: "Einstein Trust Layer probably governs data access... similarity: 0.79 " Score is on the chunk, not the claim. The claim itself is unverified. ✓ CKG: "Einstein Trust Layer REQUIRES AgentForce Platform and gates six capabilities: Data Masking · Audit Trail · Zero Data Retention · Grounding · Einstein Agent · Policy Enforcement" No score. Declared edge. Traces to trust layer source doc. 30 questions on the $2/resolution billing path, trust layer, and action types · CPU only · Ollama · temperature 0 | Category | Bare model | + CKG | Lift | |---|---|---|---| | Billing path F1 | 0.091 | 0.201 | +121% | | Trust layer F1 | 0.063 | 0.134 | +113% | | Prereq-chain F1 | 0.058 | 0.142 | +145% | | Key-fact accuracy | 8.1% | 19.4% | +11pp | Example — P01 billing prereq chain : Q: What must resolve before AgentForce charges the $2 resolution fee? ✗ Bare: "The agent completes the customer's request successfully..." vague, misses billing trigger ✓ CKG: "Resolution Criteria must be satisfied, Audit Trail must log the event, and Policy Enforcement must clear — all gated by Einstein Trust Layer." exact chain Example — L03 action type lookup : Q: What are the four AgentForce action types? ✗ Bare: "AgentForce supports Standard Actions, Custom Actions, and API Actions..." misses MuleSoft ✓ CKG: "Flow Action · Apex Action · MuleSoft Action · External Action" declared edges, correct Add to claude.ai no install required : https://ckg-agentforce.onrender.com/mcp Settings → Connectors → Add connector → paste URL. Local — Claude Desktop / Claude Code: pip install ckg-agentforce or uvx ckg-agentforce { "mcpServers": { "agentforce": { "command": "uvx", "args": "ckg-agentforce" } } } list concepts — List all 40 AgentForce concepts grouped by type search concepts — Fuzzy search across all concepts by keyword query ckg — Typed subgraph around any concept 1–5 hops get prerequisites — Full upstream prerequisite chain for any concept resolution path — The exact $2/resolution billing traversal — every hop declared verify source — Source URL + SHA-256 hash for any concept GuardrailDecisionV1 | Tool | Args | Description | |---|---|---| list concepts | — | All 40 AgentForce concepts grouped by type | search concepts query | query: str | Fuzzy search across all concepts | query ckg concept, depth | concept: str, depth: int 1–5 | Typed subgraph around any concept | get prerequisites concept | concept: str | Full upstream prerequisite chain | resolution path | — | The $2/resolution billing traversal — every hop declared | verify source concept | concept: str | Source URL + SHA-256 hash · full audit chain | 40 nodes · 52 edges · 4 edge types: REQUIRES · ENABLES · IMPLEMENTS · RELATES TO | Layer | Concepts | |---|---| | Agents | Einstein Agent · Service Agent · Sales Agent · Marketing Agent | | Actions | Flow Action · Apex Action · MuleSoft Action · External Action · Standard Action · Custom Action | | Platform | AgentForce Platform · Data Cloud · Salesforce CRM · Knowledge Base | | Reasoning | Reasoning Engine · Model Selection · NVIDIA NIM · Token Budget · Context Window | | Trust | Einstein Trust Layer · Data Masking · Zero Data Retention · Policy Enforcement · Compliance Rules | | Workflow | Agent Topic · Agent Instruction · Resolution Criteria · Conversation State · Grounding | | Billing | Autonomous Resolution $2/event · Audit Trail · Agent Metrics · Handoff to Human | | Routing | Omni-Channel Routing · Multi-Agent Orchestration · Prompt Template | Every node traces to an authoritative Salesforce source document. Every source is SHA-256 pinned. Remote MCP connector · no install · paste one URL into claude.ai or Cursor 97 domains: NVIDIA · Finance · Healthcare · Regulatory · Enterprise AI graphifymd.com/pro/ https://graphifymd.com/pro/ Every node and edge traces to one of these. No probabilistic inference — declared relationships only. | Type | Source | Coverage | |---|---|---| | Official | developer.salesforce.com/docs/einstein/genai/guide/agentforce-overview.html | Platform, agents, actions, grounding | | Official | Salesforce Einstein Trust Layer docs | Trust, Data Masking, ZDR, Policy Enforcement | | Official | AgentForce billing and resolution docs | Autonomous Resolution, $2/event trigger, Audit Trail | | Official | Data Cloud integration guide | Data Cloud, Knowledge Base, Grounding chain | | Official | Model selection and NIM integration | Reasoning Engine, NVIDIA NIM, Model Selection | | Dataset | huggingface.co/datasets/danyarm/ckg-benchmark | KRB v0.6.2 — 7,928 queries | | Benchmark | github.com/Yarmoluk/ckg-benchmark/paper/main.pdf | Full methodology, F1 0.471 | | System | Macro F1 | Mean tokens | Cost / 1k queries | |---|---|---|---| CKG | 0.471 | 269 | $7.81 | | RAG | 0.123 | 2,982 | $76.23 | | GraphRAG | 0.120 | ~3,000 | ~$76 | 7,928 queries · 5-hop F1: 0.772 CKG vs 0.170 RAG · dataset https://huggingface.co/datasets/danyarm/ckg-benchmark · full paper https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf | Layer | License | Plain English | |---|---|---| | Server code — server.py, graph.py, serve.py, scripts/ | MIT | Do anything. Fork it, embed it, sell products built on it. | | Graph data — domains/agentforce.csv + source hashes | Elastic License 2.0 | Free for all internal and commercial use. Cannot offer this graph as a competing hosted service. | | Extraction pipeline + benchmark harness | Proprietary — Graphify.md | Not in this repo. How 97 domains get built and maintained. | Can I build an agent or product using this CKG? Yes. No restrictions. Can I run this inside my company's infrastructure? Yes. ELv2 allows all internal commercial use. Can I offer "AgentForce CKG as a Service" commercially? No. That's the one thing ELv2 blocks. benchmark: ckg-benchmark v0.6.2 dataset: huggingface.co/datasets/danyarm/ckg-benchmark benchmarked: false rag baseline f1: 0.123 graphrag baseline f1: 0.120 mean tokens: 269 paper: github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf Graphify.md builds CKGs for enterprise domains — AgentForce, NVIDIA, finance, healthcare, and custom stacks. If you need a knowledge graph for your platform, product, or internal knowledge base: Try the hosted endpoint: ckg-agentforce.onrender.com https://ckg-agentforce.onrender.com — 50 free calls/day, then $29/mo https://buy.stripe.com/00wbJ1gsYcm01tC52A1kA08 Enterprise / custom domains: graphifymd.com https://graphifymd.com or email daniel.yarmoluk@gmail.com mailto:daniel.yarmoluk@gmail.com Benchmark paper: F1 0.471 · 11× fewer tokens than RAG https://github.com/Yarmoluk/ckg-benchmark/blob/main/paper/main.pdf 97 domains available: graphifymd.com/pro https://graphifymd.com/pro/ Built by Graphify.md https://graphifymd.com · 97 domains · PyPI https://pypi.org/project/ckg-agentforce/ · patent pending Community-built. Not affiliated with, endorsed by, or sponsored by Salesforce, Inc. AgentForce and Einstein are trademarks of Salesforce, Inc. All referenced trademarks belong to their respective owners.